Our working hypothesis is that the direct in vivo screening and identification of individual compounds from mixture-based combinatorial libraries will yield more """"""""advanced"""""""" therapeutic candidates, while decreasing the time and costs inherent in the drug discovery process. Current drug discovery screening strategies virtually always involve target based in vitro biochemical or cell-based assays. These serve as the primary means to identify active compounds that are next assessed for their activity in animals studies. It is at this later stage that the majority of compounds fail due to toxicity, lack of efficacy, and poor bioavailability. ? ? In support of our working hypothesis, successful preliminary studies utilizing the murine tail flick pain model have demonstrated that clear differentiation can be achieved between active and inactive mixtures. For example, a mixture of 125,000 tetrapeptides, made up of 50 different amino acids at three positions and with only its N-terminal position individually defined, was found to have antinociceptive activity in the tail flick assay. The activity of this mixture (that contained Dmt-DALDA; known to be active in vivo) had a 5 to 10 time longer duration of action than morphine, while being only 3-5 times less active than morphine on a per mg basis. Additionally, other mixtures chosen that had no activity in the mu, delta or kappa opioid binding assays were found that had clear in vivo tail flick activity. This raises the exciting possibility that novel receptor sites or multiple receptor interactions are responsible for the activities found. ? ? The two Aims in this proposal will serve as a general proof of concept for this approach and will lay the foundation for later studies with a range of existing heterocyclic mixture-based libraries.
The first Aim will use the tail flick assay to complete an iterative deconvolution process to identify the most active amino acids at the three positions of this active mixture. We believe that this will enable the identification of active individual sequences that have enhanced activity relative to Dmt-DALDA or, as a minimum, will identify Dmt-DALDA (both are acceptable proof of concept end results).
The second Aim i nvolves the in vivo screening of the entire 50 mixtures making up this tetrapeptide library of 6,250,000 different sequences (50 x 503). In addition to identifying novel opioid specific agonists, this approach may enable the identification of novel antinociceptive compounds that have activity at """"""""orphan"""""""" pain modulating non-opioid receptors. If successful, the direct in vivo testing of mixture-based combinatorial libraries will advance not only pain modulation, but biomedical research and the drug discovery process in general. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21DA019620-01A2
Application #
7258231
Study Section
Special Emphasis Panel (ZRG1-MDCN-C (91))
Program Officer
Hillery, Paul
Project Start
2007-08-01
Project End
2009-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
1
Fiscal Year
2007
Total Cost
$227,500
Indirect Cost
Name
Torrey Pines Institute for Molecular Studies
Department
Type
DUNS #
605758754
City
Port Saint Lucie
State
FL
Country
United States
Zip Code
34987
Santos, Radleigh G; Giulianotti, Marc A; Dooley, Colette T et al. (2011) Use and implications of the harmonic mean model on mixtures for basic research and drug discovery. ACS Comb Sci 13:337-44
Yongye, Austin B; Pinilla, Clemencia; Medina-Franco, Jose L et al. (2011) Integrating computational and mixture-based screening of combinatorial libraries. J Mol Model 17:1473-82
Reilley, Kate J; Giulianotti, Marc; Dooley, Colette T et al. (2010) Identification of two novel, potent, low-liability antinociceptive compounds from the direct in vivo screening of a large mixture-based combinatorial library. AAPS J 12:318-29
Singh, Narender; Guha, Rajarshi; Giulianotti, Marc A et al. (2009) Chemoinformatic analysis of combinatorial libraries, drugs, natural products, and molecular libraries small molecule repository. J Chem Inf Model 49:1010-24
Medina-Franco, Jose L; Martinez-Mayorga, Karina; Bender, Andreas et al. (2009) Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J Chem Inf Model 49:477-91
Martinez-Mayorga, Karina; Medina-Franco, Jose L; Giulianotti, Marc A et al. (2008) Conformation-opioid activity relationships of bicyclic guanidines from 3D similarity analysis. Bioorg Med Chem 16:5932-8
Houghten, Richard A; Pinilla, Clemencia; Giulianotti, Marc A et al. (2008) Strategies for the use of mixture-based synthetic combinatorial libraries: scaffold ranking, direct testing in vivo, and enhanced deconvolution by computational methods. J Comb Chem 10:3-19
Medina-Franco, Jose L; Maggiora, Gerald M; Giulianotti, Marc A et al. (2007) A similarity-based data-fusion approach to the visual characterization and comparison of compound databases. Chem Biol Drug Des 70:393-412